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[autoscaler] Experimental support for local / on-prem clusters (#2678)

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This adds some experimental (undocumented) support for launching Ray on existing nodes. You have to provide the head ip, and the list of worker ips.

There are also a couple additional utils added for rsyncing files and port-forward.
This commit is contained in:
Eric Liang
2018-08-19 12:43:04 -07:00
committed by GitHub
parent 62d0698097
commit 9473da69bd
10 changed files with 339 additions and 37 deletions
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doc/source/autoscaling.rst
+12 -2
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@@ -89,11 +89,21 @@ You can use ``ray attach`` to attach to an interactive console on the cluster.
Port-forwarding applications
----------------------------
To run connect to applications running on the cluster (e.g. Jupyter notebook) using a web browser, you can forward the port to your local machine using SSH:
To run connect to applications running on the cluster (e.g. Jupyter notebook) using a web browser, you can use the port-forward option for ``ray exec``. The local port opened is the same as the remote port:
.. code-block:: bash
$ ssh -L 8899:localhost:8899 -i <key> <user>@<addr> 'source ~/anaconda3/bin/activate tensorflow_p36 && jupyter notebook --port=8899'
$ ray exec cluster.yaml --port-forward=8899 'source ~/anaconda3/bin/activate tensorflow_p36 && jupyter notebook --port=8899'
Manually synchronizing files
----------------------------
To download or upload files to the cluster head node, use ``ray rsync_down`` or ``ray rsync_up``:
.. code-block:: bash
$ ray rsync_down cluster.yaml '/path/on/cluster' '/local/path'
$ ray rsync_up cluster.yaml '/local/path' '/path/on/cluster'
Updating your cluster
---------------------